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2) The Weekend Effect. Prices tend to rise on the last day in a week. Average return on Mondays is very small (-0.18 point), in contrast with that average return ...

U-Mart: A Test Bed for Interdisciplinary Research in Agent Based Artificial Market

Hiroshi Sato1), Yuhsuke Koyama2), Koichi Kurumatani3), Yoshinori Shiozawa4), and Hiroshi Deguchi2)

1) National Defense Academy, Hashirimizu 1-10-20, Yokosuka, Kanagawa, 239-8686, Japan e-mail: [email protected], tel: +81-468-41-3810, fax: +81-468-44-5911

2) Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan e-mail: [email protected], [email protected]

3) Electrotechnical Laboratory, Umezono 1-1-4, Tsukuba, Ibaraki, 305-8568, Japan e-mail: [email protected], url: http://www.w-econ.org

4) Osaka City University, Sugimoto 3-3-138, Sumiyoshi, Osaka, 558-0022, Japan e-mail: [email protected]

Summary U-Mart is a research project to construct a virtual stock market, which any pre-registered agent can participate in via networks. The aim of the project is to provide a forum where many agents of both computer programs and human traders participate in the auction and compete in the market, and to provide a forum where research on market structure analysis is carried out. In this paper, we introduce the concept and the system specification of U-Mart and describe its importance and research possibilities in the context of economics, computer sciences and artificial intelligence.

Key words: Artificial Market, Efficient Market Hypothesis, Multi-agents, U-Mart, SVMP

1 Introduction In this project, we provide a server service by preparing a ‘U-Mart Server’ that accept inquiries and orders from ‘U-Mart clients’ via Internet. The communication between the server and the clients is done in SVMP, Simple Virtual Market Protocol (details in session 4). The U-Mart server works as a real stock exchange market, i.e., collecting orders by agents, deciding the price, and paying/receiving the price to/from agents, except for the fact that real cash is not moved, i.e., results of trades

are just registered in the server, and that trade histories and final results will be open to all participants, that is, the aim of the project is to provide a research forum for both computer science and economics researchers to compete on improving programming and economic analysis skills in the market. In addition to it, we describe possible research topics that are expected in the project, in the context of computer science and artificial intelligence as well as economics.

2 U-Mart as an Objective for Multi-Agents Research - Comparison between U-Mart and RoboCup Soccer From the viewpoint of computer science and artificial intelligence, U-Mart is one of the standard problems where we develop and test new software techniques and computer programs to solve the problem. Especially, it is an attractive problem for multi-agent system that consists of autonomous intelligent agents. U-Mart is, in essence, influenced by a successful research project RoboCup Soccer (Kitano et al. (1997), Kitano et al. (1995)), mainly in the idea that it encourages fast development of new ideas and techniques to prepare an open forum where many software gather and compete each other.

U-Mart has, however, special characteristics as a standard problem that conventional techniques have not confronted. We describe these characteristics by contrasting U-Mart with RoboCup Soccer. 1. In a market, there is obviously no special person nor program whom a certain program should compete with. It should compete with the market itself. Program developers should investigate a market as a whole that consists of many kinds of agents rather than a person or a team, which increases the complexity of computation. 2. A game played in a market is non zero-sum. All players have possibilities to gain or lose simultaneously. 3. A market is a real open environment that is frequently influenced by information from other social systems, in contrast with RoboCup Soccer as a closed environment. 4. The duration of game is longer. Usually it takes a couple of weeks, months, or possibly years to decide winners. This means that players must have a long-term perspective in addition to short-term interest that can be gained for a short period. The comparison between U-Mart and RoboCup Soccer is summarized in Table 1. Table1

3 U-Mart as an new research program for economics - Comparison between U-Mart and ordinal economic paradigm Recently, economics adopts various arguments from various sciences such as computer science, artificial intelligence, decision theory, and behavioral science. Economics is now changing remarkably. In this section, we introduce existent arguments about the stock market around economics.

3. 1 Efficient Market Hypothesis Economists have regarded a stock market as one of the most efficient markets because the prices are adjusted rapidly, huge amount of exchanges are quickly processed by computers, and market participants needs negligible costs to receive information about market circumstances. Standard and simplest efficient market theory assumes identical homogenous investors who share rational expectations of an asset’s future price. In the neoclassical framework, the market is a description of an exogenous mechanism for selecting prices that equalize aggregate supply and demand. We try to think about the simplest model of the efficient market hypothesis. Consider a market with single security that provides a stochastic dividend sequence dt, with a risk-free outside asset that pays a constant r units per period. Assuming that all agents from expectations of

the price and dividend in the next period under the common current information It, and that they arbitrate perfectly, the price pt at time t is given as pt=(1+r)-1E[pt+1+dt+1|It], where E[] is expected value. The market clears at the rational expectation equilibrium price pt given by successive substitution: pt*=S (1+r)-1E[dt+n |It]. The only source of price fluctuation is the change in the common information and the dividend policy when the interest rate is constant. On the other hand, because all agents form the identical expectation of the price, there is no incentive to spectrum and the efficient market theory cannot explain complex behaviors of a market that emerge from the interaction with heterogeneous agents, e.g., bubbles and clashes.

3. 2 Some Anomalies in Efficient Market Hypothesis There is a controversy whether the efficient market hypothesis can explain the reality in markets. Neoclassical economists tend to support it, but psychologists and behaviorists are incredulous about it. Thaler (1992) collects anomalies that are against the efficient market hypothesis. 1) The January Effect The average monthly return to the small firms in January was much higher than in other months. Half of excess returns to small firms came in January, and half of them came in the first fifth trading days. The January effect

returns were higher for small firms that had lost in value during the previous year, and the excess returns in the first five days 2) The Weekend Effect Prices tend to rise on the last day in a week. Average return on Mondays is very small (-0.18 point), in contrast with that average return on Fridays (or Saturdays) is 0.12 point. Price on Monday rises from the opening to the closing. The Price of the smaller firms shows greater changes in the weekend effect. 3) Holidays Dow Johns Industrial Average (DJIA) showed a high proportion of advances the day before holidays. 4) Turn-of-the-Month Effects All the returns for a month occur in the first half of the month, the returns for the latter half of the month is negative. 5) Intraday Effects On all days without Monday, prices rise during first 45 minutes. Returns are high near the end of the day, particularly on the last trade of the day. The day-end price changes are greatest during last five minutes, have been observer in experimental markets. Thaler (1992) also said a part of these phenomena can be explained by the structural and institutional reasons such as 1) the difference of duration

when a market is closed and 2) the special duration related to a settling day. We need, however, other approaches to model the phenomena in order to explain them totally.

3. 3 Noise trader approach There are various types of traders in the real market. From the motivation to the dealings, traders are classified under three types, the informed traders, the uninformed traders, and the market makers. The informed traders have some information that is based on the economic fundamentals and not common to market participants, and utilize it for the dealing to maximize their profits. Some uninformed traders have neither information nor interest for speculation, trade for liquidity and purchase at the ask price or sell the lid price, depending on their estimates of the asset value of liquidity requirements. They are called liquidity traders. The other uninformed traders also have some information, which is not based on the fundamentals but on the technical analysis, that is, market trends. Technical analysis typically calls for buying more stocks when stocks have risen (break through a barrier), and selling stocks when they fall through a floor. Both liquidity traders and technical analysts are often called “noise traders” (Shleifer and Summers (1990)).

The market makers must deal with informed and uninformed traders. They are worked as market intermediaries whose main function is to figure out ways of clearing the market, e.g., pricing to match to purchase to sales. Some market participants have expectations based on market trends in the real market, which means that the investors can follow the technical analysts and make use of the fluctuations of the market in order to make more profit. The investment plan based on the economic fundamentals is not always the profit-maximizing behavior. It is important for the investment success not to predict future fundamentals precisely, but also to predict the movement of other active investors. Trend chasers’ behavior, buy after stock prices rise and sell after they fall, amplifies the fluctuations of the market, e.g., they follow positive feedback strategies.

3. 4 Individual decision making under risk and uncertainty After Allais (1953), it has been well known for more than forty years that individual decision makers do not behave in accordance with the axioms of expected utility theory. In addition, because what kind of fundamental information should be cared or how information should be decoded have not been completely settled out, traders’ decisions about sells and buys depend on their cognitive frameworks and sentiments, which are in the

research area for the cognitive psychologists and the decision theorists. 1) Scenario Thinking Kahneman and Tversky (1973) showed a common cognitive error, known as scenario thinking, that the guess of occurrence probability is influenced by the easiness and the availability to imagine a phenomenon, which they called the “bias of imaginability”. Though it is necessary in the cases where there is little information about the future, it must be recognized that scenario thinking sometimes extrapolates excess optimisms and pessimisms about the market. 2) Prospect Theory Kahneman and Tversky (1979) extended the expected utility theory and described a model of decision making under risk and uncertainty. The individual’s evaluation function is 1) defined on deviations from the reference point, 2) generally concave for gains and commonly convex for losses, 3) steeper for losses than for gains, e.g., risk averse. Decision weight function p(p) on the probability p is 1) generally lower than the corresponding probabilities 2) except in the range of low probabilities. Overweighing of low probabilities may contribute to the attractiveness of speculative trading. In addition, as prices are always evaluated from relative changes, the sequential movement of prices may influence the individual’s judgments of buys and sells.

3. 5 Artificial Stock Market Arthur et al. (1997) proposed the ASM (Artificial Stock Market) approach where all agents are bounded rational and form price expectations like human uninformed traders. Each agent has 100 predictors that are used for GA (Genetic Algorithm) learning for technical trading, and 25 agents participate in the artificial market. Izumi and Ueda (2000) integrate the fieldwork and the multi-agent model. They interviewed a dealer and found that the features of the dealer interaction in learning were similar to features of genetic operations in biology. They constructed an artificial market model with 100 agents using a Genetic Algorithm; each agent has prediction method in imitation of a dealer. Their model also showed the several emergent phenomena: the peaked and long-tailed distribution of rate changes, the negative correlation between trading amounts and fluctuation, and the contrary opinion phenomena, by the phase transition.

3. 6 U-mart as a hybrid approach These ASMs can express various kinds of the market phases. However, agents have the capability of technical trading already programmed, and the period of moving average is fixed, which might cause systemic oscillations.

After all, varieties of agents that the ASM approach can express are limited. One of the important characteristics of U-Mart is its openness. It consists of public participants pre-registered to the market. The market is expected to consist of two types of agents, i.e., machine automatic agents and human agents. Because there always exist various kinds of price expectations that bring diversity to the market, we can investigate complex interactions between machine and human agents (see Table 2). Table 2 4 U-Mart Experimental System In this section, we introduce the artificial market simulator system and the communication protocol that are developed by U-Mart organizing committee.

4. 1 System Specification Figure 1 shows the composition of U-Mart experimental system. The system consists of virtual market server and trader clients. The clients communicate with the server by Simple Virtual Market Protocol (SVMP). SVMP is character-based protocol on TCP/IP. This means that the server and clients can be developed separately and the program is platform-free. U-Mart server contains communication component and database

component in addition to transaction component. SVMP is settled for communication and PostgreSQL is used for database. The system is implemented by Java language because Java eases network programming and database programming. Fig. 1 4. 1. 1 Transaction Specification U-mart adopts the interval time trading; Orders are accepted at any time, but contracts are enforced at each discrete time step. This is because the delay of the Internet. The traders have to be a member of the market. Some amount of virtual money is given to the member for trading. Order is restricted by consideration of cash and position of the member in order to prohibit the reckless order. When each virtual trading day is finished, market calculates the property of the members by speculation from spot price. If the capital of the member is lesser than a criterion, he/she goes bankrupt and cannot continue trading.

4. 1. 2 Articles Specification U-Mart server deals with virtual futures of the real stock index. This can establishes the one-way connection to the real world. We are listing futures of J30, the stock index of Japanese representative 30 companies. Detailed

information is as follows: article: j30 delivery month: 3 months final settlement day: next business day of final trade day method of final settlement: speculating for difference from spot price maximum price change limit: none

4. 1. 3 Transaction Specification There are two kinds of orders; ordinarily order and cancel order. Ordinarily order takes five attributes and cancel order has three attributes as follows: ordinarily order order ID: added by server (integer) new or reverse (1: new, 2: reverse) sell or buy (1: sell, 2: buy) market price or limit price (1: market, 2: limit) volume (integer) cancel order order ID (1): added by server (integer) order ID (2): that you want to cancel (integer) volume (integer) The server holds all contracts for a new position and balances for an

existence position. Since we deal stock index futures, settlement is done by speculating for difference from the last price. Final settlement is done by speculating for difference from the spot price.

4. 2 Simple Virtual Market Protocol U-Mart organizing committee has been designed the protocol for the communication between the server and clients. In this section, we introduce Simple Virtual Market Protocol (SVMP).

4. 2. 1 Connection Between Server and Clients The server and clients communicate through TCP/IP using socket interface. The port number for U-Mart is 5010. The status of connection is comprised of connected-status and non connected-status. First of all, trader sends the ID and password to the server, and after the certification, it begins transaction.

4. 2. 2 Format of Message Messages are divided into following three types: (1) prompt (server -> client), (2) request (client -> server), and (3) result (server -> client). Here is a brief specification of the message format. [available character] ASCII characters

[word] one or more characters divided by delimiter [delimiter] space or tab [maximum length] line: 15 words, word: 64 characters [date and time] YYYY/MM/DD, HH:MM:SS [prompt] before certification: “+LOGIN”, after certification: “+OK” [request] instruction argument1 argument2 … [result] normal condition: “+ACCEPT”, illegal condition: “+ERROR”

4. 2. 3 Commands Table 3 shows the main command of U-Mart. Commands are classified into following four categories: (1) certification, (2) status of server, (3) order, and (4) inquiry. There are other commands for administrator only. Table 3 4. 3 TOOLS Basically, the server is a character-base program, but graphical components can be attached to the server. If we execute U-Mart with graphical components, parameters are set through widget (Figure 2) and results are shown in charts on window (Figure 3). U-Mart experiment system includes three types of sample agents and GUI for human trader. Sample agents are: (1) random-trader that attempts to buy and sell randomly, (2) trend-trader that attempts buy if the price

leaps, sell if the price drops, and (3) anti-trend-trader that is exact opposite of trend-trading agent. The volume of their transaction is determined by random. The traders decide their investment based on the move of spot price or futures price. You can control the connectivity to real world by proportion of spot price conscious agents in the market: If you increase spot price conscious agents, connection to the real stocks is strengthen, and vise versa. Graphical components for U-Mart server that consists of three windows are shown in Figure 3. Left-upper window is the chart of futures and spot price. Right-upper window depicts the supply and demand line. The point of intersection means contract price. Left-lower window shows the Fig. 3

properties of all traders. Figure 4 shows the graphical user interface for human trader. The GUI has four windows. Left-upper window is the property of the trader. Right-upper window depicts price chart. Left-lower window draws the time sequential of the trader's profit. The trader can request or cancel order through right-lower window.

Fig. 4

At the present time, U-Mart server can establish hundreds of connection and accept thousands of order in one second.

5 Conclusion We introduced the outline of a research project ‘U-Mart’ as a forum of multi-agent system research, and as a research objective of market structure analysis. The U-Mart committee distributes the U-Mart training kit at U-Mart web page (http://u-mart.econ.kyoto-u.ac.jp/). Any researchers and actual traders will be welcome to the market in order to prove and improve their programming techniques and skills in trading.

References Allais M (1953) Le comprtement de l’Homme Rational devant la Risque, Critique des Postulats et Axiomes de l’Ecole Americane. Econometrica 21: 503-46

Arthur WB, Holland JH, LeBaron B, and Tayler P (1997) Asset Pricing Under Endogenous Expectations in an Artificial Stock Market. SFI Studies in the Sciences of Complexity, Vol. XXVII. Addison-Wesley

Izumi K, Ueda K (1998) Emergent Phenomena in a Foreign Exchange Market: Analysis Based on an Artificial Market Approach. In: Adami C, Belew R, Kitano H, Taylor C (Eds) Artificial Life VI, Cambridge, MA: MIT Press, pp 398-402

Kahneman D, Tversky A (1979) Prospect theory: An Analysis of Decision Under Risk. Econometrica 47: 263-91

Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E, Matsubara H (1995) Robocup: The robot world cup initiative. In: Proceedings of Workshop on Entertainment and AI/Alife, IJCAI’95

Kitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E, Matsubara H (1997) Robocup: A challenge problem for AI. AAAI AI magazine 18(1): 73-85.

Shleifer A, Summers, LH (1990) The Noise Trader Approach to Finance. Journal of Economic Perspectives, Vol. 4, No. 2: 19-33

Thaler RH (1992) The Winner’s Curse – Paradoxes and Anomalies of Economic Life, The Free Press.

Tversky A, Kahneman, D (1973) Availability: A Heuristic for Judging Frequency and Probability. Cognitive Psychology 5: 207-32

Table 1. Comparison between U-Mart and RoboCup Investigation Object U-Mart

Market Itself

RoboCup Soccer

Opposite Team

Number of Agent Much More O(100) –O(10000) O(10)

Game Type

Openness Game Duration Long Non Zero-Sum Open O(1 month) Short Zero-Sum Closed O(10 min)

Table 2. Comparison Micro-Structure




Agent Type



Complex Behaviors

Efficient Market Hypothesis Noise Trader Approach Artificial Stock Market

Homogenous Heterogeneous Homogenous

Fixed Fixed Learned

Closed Closed Closed

Impossible Possible Possible



Any Type





Table 3. Command List of Simple Virtual Market Protocol Command Login Logout Passwd Schedule StartTime MarketStatus ProtocolVersion IntervalTime ServerTime ServerDate OrderRequest OrderCancel OrderStatus Executions Position Balances TodaysQuotes HistoricalQuotes BoardInformation ContractSpecification SpotPrice FuturesPrice

Arguments ID, password old passwd, new passwd brand ID, new/reverse, sell/buy, market/limit, price, volume order ID brand ID, length brand ID, length brand ID article/brand, article ID/brand ID, assortment brand ID, length brand ID, length

Function logging in logging out change password trade schedule start time status of server version of SVMP interval of contract time of server date of server order cancel order inquiry of order inquiry of contract inquiry of position inquiry of balance quotes of today History of quotes aggregate of orders Specification of transaction spot price price of futures

First category is for “certification.” Second category is for “status of server.” Third category is for “order.” Forth category is “inquiry.”

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Figure Legend

Fig. 1. Composition of U-Mart experimental system

Fig.2. Parameter Widget for U-Mart Server

Fig. 3. Graphical User Interface for U-Mart Server. Left upper widget is a price chart: spot price (blue) and futures price (red). Right upper widget is contract chart: buy curve (blue) and sell curve (red). Left lower widget shows a position list of all traders

Fig. 4. Graphical User Interface for U-Mart Client. Left upper widget shows information of client’s position. Right upper widget is a price chart: spot price (blue) and futures price (red). Left lower widget shows time evolution of client’s profit. Right lower widget is an order form

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